Overview

Dataset statistics

Number of variables14
Number of observations2398116
Missing cells1427778
Missing cells (%)4.3%
Duplicate rows355
Duplicate rows (%)< 0.1%
Total size in memory256.1 MiB
Average record size in memory112.0 B

Variable types

Numeric11
Categorical3

Alerts

Dataset has 355 (< 0.1%) duplicate rowsDuplicates
mother_body_mass_index is highly overall correlated with mother_delivery_weightHigh correlation
mother_delivery_weight is highly overall correlated with mother_body_mass_indexHigh correlation
father_education is highly overall correlated with mother_marital_statusHigh correlation
mother_marital_status is highly overall correlated with father_educationHigh correlation
previous_cesarean is highly imbalanced (60.0%)Imbalance
mother_body_mass_index has 146600 (6.1%) missing valuesMissing
mother_marital_status has 412510 (17.2%) missing valuesMissing
mother_delivery_weight has 34958 (1.5%) missing valuesMissing
mother_height has 244529 (10.2%) missing valuesMissing
mother_weight_gain has 73473 (3.1%) missing valuesMissing
father_age has 444506 (18.5%) missing valuesMissing
number_prenatal_visits has 59901 (2.5%) missing valuesMissing
mother_weight_gain has 68723 (2.9%) zerosZeros
cigarettes_before_pregnancy has 2186624 (91.2%) zerosZeros
prenatal_care_month has 40409 (1.7%) zerosZeros
number_prenatal_visits has 40409 (1.7%) zerosZeros

Reproduction

Analysis started2023-05-09 15:03:41.819591
Analysis finished2023-05-09 15:06:05.082119
Duration2 minutes and 23.26 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

mother_body_mass_index
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct561
Distinct (%)< 0.1%
Missing146600
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean27.16721
Minimum13
Maximum69.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 MiB
2023-05-09T17:06:05.205873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile19
Q122.3
median25.7
Q330.7
95-th percentile40.3
Maximum69.8
Range56.8
Interquartile range (IQR)8.4

Descriptive statistics

Standard deviation6.7557576
Coefficient of variation (CV)0.24867322
Kurtosis1.7558702
Mean27.16721
Median Absolute Deviation (MAD)4
Skewness1.1826656
Sum61167408
Variance45.640261
MonotonicityNot monotonic
2023-05-09T17:06:05.322091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.6 41362
 
1.7%
28.3 36430
 
1.5%
23 29869
 
1.2%
22.3 28077
 
1.2%
25.8 27046
 
1.1%
27.4 26855
 
1.1%
21.3 25838
 
1.1%
21.9 25337
 
1.1%
21.6 25100
 
1.0%
25.7 25015
 
1.0%
Other values (551) 1960587
81.8%
(Missing) 146600
 
6.1%
ValueCountFrequency (%)
13 8
 
< 0.1%
13.1 14
 
< 0.1%
13.2 21
 
< 0.1%
13.3 28
< 0.1%
13.4 26
< 0.1%
13.5 29
< 0.1%
13.6 33
< 0.1%
13.7 63
< 0.1%
13.8 27
< 0.1%
13.9 48
< 0.1%
ValueCountFrequency (%)
69.8 2
 
< 0.1%
69.7 2
 
< 0.1%
69.5 3
 
< 0.1%
69.4 1
 
< 0.1%
69.3 2
 
< 0.1%
69.1 5
 
< 0.1%
68.9 1
 
< 0.1%
68.8 3
 
< 0.1%
68.7 29
< 0.1%
68.6 1
 
< 0.1%

mother_marital_status
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing412510
Missing (%)17.2%
Memory size18.3 MiB
1.0
1192238 
2.0
793368 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5956818
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 1192238
49.7%
2.0 793368
33.1%
(Missing) 412510
 
17.2%

Length

2023-05-09T17:06:05.458582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T17:06:05.718073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1192238
60.0%
2.0 793368
40.0%

Most occurring characters

ValueCountFrequency (%)
. 1985606
33.3%
0 1985606
33.3%
1 1192238
20.0%
2 793368
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3971212
66.7%
Other Punctuation 1985606
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1985606
50.0%
1 1192238
30.0%
2 793368
 
20.0%
Other Punctuation
ValueCountFrequency (%)
. 1985606
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5956818
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1985606
33.3%
0 1985606
33.3%
1 1192238
20.0%
2 793368
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5956818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1985606
33.3%
0 1985606
33.3%
1 1192238
20.0%
2 793368
 
13.3%

mother_delivery_weight
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct301
Distinct (%)< 0.1%
Missing34958
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean188.31698
Minimum100
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 MiB
2023-05-09T17:06:05.825385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile135
Q1159
median181
Q3210
95-th percentile267
Maximum400
Range300
Interquartile range (IQR)51

Descriptive statistics

Standard deviation41.369241
Coefficient of variation (CV)0.21967876
Kurtosis1.6461613
Mean188.31698
Median Absolute Deviation (MAD)25
Skewness1.0477953
Sum4.4502278 × 108
Variance1711.4141
MonotonicityNot monotonic
2023-05-09T17:06:05.948074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 47979
 
2.0%
180 47202
 
2.0%
170 44111
 
1.8%
150 40506
 
1.7%
165 37313
 
1.6%
190 35472
 
1.5%
200 34388
 
1.4%
175 33954
 
1.4%
185 31250
 
1.3%
155 28049
 
1.2%
Other values (291) 1982934
82.7%
(Missing) 34958
 
1.5%
ValueCountFrequency (%)
100 1147
< 0.1%
101 184
 
< 0.1%
102 247
 
< 0.1%
103 285
 
< 0.1%
104 272
 
< 0.1%
105 556
< 0.1%
106 427
 
< 0.1%
107 463
< 0.1%
108 662
< 0.1%
109 560
< 0.1%
ValueCountFrequency (%)
400 1209
0.1%
399 47
 
< 0.1%
398 67
 
< 0.1%
397 41
 
< 0.1%
396 54
 
< 0.1%
395 56
 
< 0.1%
394 38
 
< 0.1%
393 47
 
< 0.1%
392 55
 
< 0.1%
391 49
 
< 0.1%

mother_race
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5223421
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 MiB
2023-05-09T17:06:06.053795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1115543
Coefficient of variation (CV)0.73016062
Kurtosis5.978017
Mean1.5223421
Median Absolute Deviation (MAD)0
Skewness2.5217597
Sum3650753
Variance1.2355529
MonotonicityNot monotonic
2023-05-09T17:06:06.143741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1764845
73.6%
2 379173
 
15.8%
4 159734
 
6.7%
6 63288
 
2.6%
3 23241
 
1.0%
5 7835
 
0.3%
ValueCountFrequency (%)
1 1764845
73.6%
2 379173
 
15.8%
3 23241
 
1.0%
4 159734
 
6.7%
5 7835
 
0.3%
6 63288
 
2.6%
ValueCountFrequency (%)
6 63288
 
2.6%
5 7835
 
0.3%
4 159734
 
6.7%
3 23241
 
1.0%
2 379173
 
15.8%
1 1764845
73.6%

mother_height
Real number (ℝ)

Distinct46
Distinct (%)< 0.1%
Missing244529
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean64.121252
Minimum30
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 MiB
2023-05-09T17:06:06.250728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile60
Q162
median64
Q366
95-th percentile69
Maximum78
Range48
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8355252
Coefficient of variation (CV)0.044221301
Kurtosis0.79989766
Mean64.121252
Median Absolute Deviation (MAD)2
Skewness0.080771473
Sum1.380907 × 108
Variance8.0402031
MonotonicityNot monotonic
2023-05-09T17:06:06.375939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
64 319699
13.3%
63 281534
11.7%
62 273071
11.4%
65 257357
10.7%
66 237395
9.9%
67 187786
7.8%
61 155167
6.5%
60 119183
 
5.0%
68 103323
 
4.3%
69 69668
 
2.9%
Other values (36) 149404
6.2%
(Missing) 244529
10.2%
ValueCountFrequency (%)
30 10
< 0.1%
31 2
 
< 0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
36 7
< 0.1%
37 3
 
< 0.1%
38 3
 
< 0.1%
39 7
< 0.1%
40 2
 
< 0.1%
41 3
 
< 0.1%
ValueCountFrequency (%)
78 438
 
< 0.1%
77 266
 
< 0.1%
76 269
 
< 0.1%
75 513
 
< 0.1%
74 1401
 
0.1%
73 2774
 
0.1%
72 8406
 
0.4%
71 18487
 
0.8%
70 34409
1.4%
69 69668
2.9%

mother_weight_gain
Real number (ℝ)

MISSING  ZEROS 

Distinct99
Distinct (%)< 0.1%
Missing73473
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean29.483728
Minimum0
Maximum98
Zeros68723
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size18.3 MiB
2023-05-09T17:06:06.501834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q120
median29
Q338
95-th percentile55
Maximum98
Range98
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.146299
Coefficient of variation (CV)0.51371723
Kurtosis1.0413842
Mean29.483728
Median Absolute Deviation (MAD)9
Skewness0.54174921
Sum68539142
Variance229.41038
MonotonicityNot monotonic
2023-05-09T17:06:06.632218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 110254
 
4.6%
20 84648
 
3.5%
25 80517
 
3.4%
35 73636
 
3.1%
0 68723
 
2.9%
40 67846
 
2.8%
28 64233
 
2.7%
27 61887
 
2.6%
32 61150
 
2.5%
33 59945
 
2.5%
Other values (89) 1591804
66.4%
(Missing) 73473
 
3.1%
ValueCountFrequency (%)
0 68723
2.9%
1 8937
 
0.4%
2 10713
 
0.4%
3 11199
 
0.5%
4 12452
 
0.5%
5 16264
 
0.7%
6 15585
 
0.6%
7 16925
 
0.7%
8 19003
 
0.8%
9 19008
 
0.8%
ValueCountFrequency (%)
98 3513
0.1%
97 181
 
< 0.1%
96 215
 
< 0.1%
95 320
 
< 0.1%
94 232
 
< 0.1%
93 228
 
< 0.1%
92 265
 
< 0.1%
91 251
 
< 0.1%
90 725
 
< 0.1%
89 336
 
< 0.1%

father_age
Real number (ℝ)

Distinct78
Distinct (%)< 0.1%
Missing444506
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean31.801093
Minimum11
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 MiB
2023-05-09T17:06:06.761160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile21
Q127
median31
Q336
95-th percentile44
Maximum98
Range87
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8126466
Coefficient of variation (CV)0.21422681
Kurtosis0.90039787
Mean31.801093
Median Absolute Deviation (MAD)4
Skewness0.55440606
Sum62126933
Variance46.412153
MonotonicityNot monotonic
2023-05-09T17:06:06.876833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 120181
 
5.0%
31 118844
 
5.0%
33 116729
 
4.9%
30 116504
 
4.9%
29 109740
 
4.6%
34 109318
 
4.6%
28 103843
 
4.3%
35 101883
 
4.2%
27 95096
 
4.0%
36 91339
 
3.8%
Other values (68) 870133
36.3%
(Missing) 444506
18.5%
ValueCountFrequency (%)
11 1
 
< 0.1%
12 2
 
< 0.1%
13 12
 
< 0.1%
14 101
 
< 0.1%
15 444
 
< 0.1%
16 1627
 
0.1%
17 4628
 
0.2%
18 10652
 
0.4%
19 19721
0.8%
20 29402
1.2%
ValueCountFrequency (%)
98 1
 
< 0.1%
91 1
 
< 0.1%
88 1
 
< 0.1%
85 1
 
< 0.1%
84 2
 
< 0.1%
83 6
< 0.1%
82 1
 
< 0.1%
81 2
 
< 0.1%
80 2
 
< 0.1%
79 2
 
< 0.1%

father_education
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9042407
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 MiB
2023-05-09T17:06:06.974381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3058055
Coefficient of variation (CV)0.47016565
Kurtosis-0.89317692
Mean4.9042407
Median Absolute Deviation (MAD)2
Skewness0.44140214
Sum11760938
Variance5.316739
MonotonicityNot monotonic
2023-05-09T17:06:07.071516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 619048
25.8%
6 412201
17.2%
4 386863
16.1%
9 329016
13.7%
2 186785
 
7.8%
7 162676
 
6.8%
5 151284
 
6.3%
1 78382
 
3.3%
8 71861
 
3.0%
ValueCountFrequency (%)
1 78382
 
3.3%
2 186785
 
7.8%
3 619048
25.8%
4 386863
16.1%
5 151284
 
6.3%
6 412201
17.2%
7 162676
 
6.8%
8 71861
 
3.0%
9 329016
13.7%
ValueCountFrequency (%)
9 329016
13.7%
8 71861
 
3.0%
7 162676
 
6.8%
6 412201
17.2%
5 151284
 
6.3%
4 386863
16.1%
3 619048
25.8%
2 186785
 
7.8%
1 78382
 
3.3%
Distinct67
Distinct (%)< 0.1%
Missing11301
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.1043881
Minimum0
Maximum98
Zeros2186624
Zeros (%)91.2%
Negative0
Negative (%)0.0%
Memory size18.3 MiB
2023-05-09T17:06:07.209990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum98
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.7305202
Coefficient of variation (CV)4.2833858
Kurtosis74.378488
Mean1.1043881
Median Absolute Deviation (MAD)0
Skewness6.8454906
Sum2635970
Variance22.377821
MonotonicityNot monotonic
2023-05-09T17:06:07.333359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2186624
91.2%
20 60078
 
2.5%
10 53333
 
2.2%
5 21147
 
0.9%
3 9887
 
0.4%
2 7970
 
0.3%
4 7551
 
0.3%
1 6183
 
0.3%
40 5996
 
0.3%
6 5937
 
0.2%
Other values (57) 22109
 
0.9%
(Missing) 11301
 
0.5%
ValueCountFrequency (%)
0 2186624
91.2%
1 6183
 
0.3%
2 7970
 
0.3%
3 9887
 
0.4%
4 7551
 
0.3%
5 21147
 
0.9%
6 5937
 
0.2%
7 3680
 
0.2%
8 3411
 
0.1%
9 648
 
< 0.1%
ValueCountFrequency (%)
98 439
< 0.1%
95 1
 
< 0.1%
92 1
 
< 0.1%
90 46
 
< 0.1%
88 6
 
< 0.1%
85 2
 
< 0.1%
84 2
 
< 0.1%
80 338
< 0.1%
76 1
 
< 0.1%
75 4
 
< 0.1%

prenatal_care_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2958756
Minimum0
Maximum99
Zeros40409
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size18.3 MiB
2023-05-09T17:06:07.439987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile7
Maximum99
Range99
Interquartile range (IQR)1

Descriptive statistics

Standard deviation15.055082
Coefficient of variation (CV)2.8427937
Kurtosis34.39938
Mean5.2958756
Median Absolute Deviation (MAD)1
Skewness6.0014092
Sum12700124
Variance226.65549
MonotonicityNot monotonic
2023-05-09T17:06:07.530063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 941813
39.3%
3 730167
30.4%
4 210075
 
8.8%
1 139262
 
5.8%
5 105509
 
4.4%
6 65178
 
2.7%
99 59760
 
2.5%
7 51778
 
2.2%
0 40409
 
1.7%
8 38619
 
1.6%
Other values (2) 15546
 
0.6%
ValueCountFrequency (%)
0 40409
 
1.7%
1 139262
 
5.8%
2 941813
39.3%
3 730167
30.4%
4 210075
 
8.8%
5 105509
 
4.4%
6 65178
 
2.7%
7 51778
 
2.2%
8 38619
 
1.6%
9 15276
 
0.6%
ValueCountFrequency (%)
99 59760
 
2.5%
10 270
 
< 0.1%
9 15276
 
0.6%
8 38619
 
1.6%
7 51778
 
2.2%
6 65178
 
2.7%
5 105509
 
4.4%
4 210075
 
8.8%
3 730167
30.4%
2 941813
39.3%

number_prenatal_visits
Real number (ℝ)

MISSING  ZEROS 

Distinct81
Distinct (%)< 0.1%
Missing59901
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean11.293179
Minimum0
Maximum98
Zeros40409
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size18.3 MiB
2023-05-09T17:06:07.639440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median12
Q313
95-th percentile18
Maximum98
Range98
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.197046
Coefficient of variation (CV)0.37164435
Kurtosis5.8723265
Mean11.293179
Median Absolute Deviation (MAD)2
Skewness0.6245808
Sum26405880
Variance17.615195
MonotonicityNot monotonic
2023-05-09T17:06:07.765461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 370513
15.5%
10 313608
13.1%
11 238126
9.9%
13 229898
9.6%
14 193076
8.1%
9 155579
 
6.5%
15 146015
 
6.1%
8 130915
 
5.5%
7 82978
 
3.5%
16 75910
 
3.2%
Other values (71) 401597
16.7%
ValueCountFrequency (%)
0 40409
 
1.7%
1 10725
 
0.4%
2 17729
 
0.7%
3 25167
 
1.0%
4 34934
 
1.5%
5 49750
 
2.1%
6 66832
2.8%
7 82978
3.5%
8 130915
5.5%
9 155579
6.5%
ValueCountFrequency (%)
98 1
 
< 0.1%
97 1
 
< 0.1%
90 2
< 0.1%
89 1
 
< 0.1%
84 3
< 0.1%
77 1
 
< 0.1%
76 3
< 0.1%
75 4
< 0.1%
74 1
 
< 0.1%
73 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
N
2020874 
Y
375672 
U
 
1570

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2398116
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 2020874
84.3%
Y 375672
 
15.7%
U 1570
 
0.1%

Length

2023-05-09T17:06:07.877109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T17:06:07.970281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
n 2020874
84.3%
y 375672
 
15.7%
u 1570
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 2020874
84.3%
Y 375672
 
15.7%
U 1570
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2398116
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 2020874
84.3%
Y 375672
 
15.7%
U 1570
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2398116
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2020874
84.3%
Y 375672
 
15.7%
U 1570
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2398116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2020874
84.3%
Y 375672
 
15.7%
U 1570
 
0.1%

newborn_gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
M
1225891 
F
1172225 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2398116
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
M 1225891
51.1%
F 1172225
48.9%

Length

2023-05-09T17:06:08.056135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-09T17:06:08.146730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
m 1225891
51.1%
f 1172225
48.9%

Most occurring characters

ValueCountFrequency (%)
M 1225891
51.1%
F 1172225
48.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2398116
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 1225891
51.1%
F 1172225
48.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 2398116
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1225891
51.1%
F 1172225
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2398116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 1225891
51.1%
F 1172225
48.9%

newborn_weight
Real number (ℝ)

Distinct5195
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3261.8353
Minimum227
Maximum8165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 MiB
2023-05-09T17:06:08.241921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum227
5-th percentile2268
Q12960
median3300
Q33629
95-th percentile4120
Maximum8165
Range7938
Interquartile range (IQR)669

Descriptive statistics

Standard deviation590.47237
Coefficient of variation (CV)0.18102458
Kurtosis2.7221499
Mean3261.8353
Median Absolute Deviation (MAD)330
Skewness-0.86348702
Sum7.8222595 × 109
Variance348657.62
MonotonicityNot monotonic
2023-05-09T17:06:08.369295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3260 26534
 
1.1%
3430 25512
 
1.1%
3090 23031
 
1.0%
3600 22025
 
0.9%
3345 20816
 
0.9%
3175 20195
 
0.8%
3515 18844
 
0.8%
3402 18425
 
0.8%
3289 18239
 
0.8%
3374 17924
 
0.7%
Other values (5185) 2186571
91.2%
ValueCountFrequency (%)
227 87
< 0.1%
228 2
 
< 0.1%
229 5
 
< 0.1%
230 18
 
< 0.1%
231 1
 
< 0.1%
232 8
 
< 0.1%
233 2
 
< 0.1%
235 12
 
< 0.1%
236 6
 
< 0.1%
237 2
 
< 0.1%
ValueCountFrequency (%)
8165 7
< 0.1%
8160 1
 
< 0.1%
7975 1
 
< 0.1%
7940 2
 
< 0.1%
7815 1
 
< 0.1%
7757 1
 
< 0.1%
7730 1
 
< 0.1%
7710 1
 
< 0.1%
7626 1
 
< 0.1%
7352 1
 
< 0.1%

Interactions

2023-05-09T17:05:45.946510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:36.356134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:43.452100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:50.720498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:57.800627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:04.774408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:11.852945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:18.249277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:25.184853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:31.979955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:38.920018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:46.727335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:37.126628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:44.154885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:51.358091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-05-09T17:05:12.449302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:18.944735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-05-09T17:05:32.642009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-05-09T17:05:06.149709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-05-09T17:05:19.626581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:26.483902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:33.323800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:40.260500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-05-09T17:05:13.566331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:20.190439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:27.054291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-05-09T17:05:40.861792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:48.693801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:38.997844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:46.146431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:53.221487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:00.301077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:07.400527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:14.147843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:21.026836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:27.695218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:34.561484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:41.521964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:49.259257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:39.541815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:46.731186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:53.756720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:00.832051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:07.960083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:14.705434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:21.552328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-05-09T17:05:49.943819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:40.209867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-05-09T17:05:08.623055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-05-09T17:05:35.783665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:42.764697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:50.597779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:40.853362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:48.089934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:55.071801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:02.141340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:09.289303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:15.860617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:22.754466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:29.490467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:36.408874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:43.411751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:51.547036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:41.506616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:48.771793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:55.868589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:02.776124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:09.958159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:16.442458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:23.367243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:30.088908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:37.018059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:44.068692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:52.205558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:42.145148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:49.436286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:56.480916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:03.410302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:10.603818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:16.995357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:23.964062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:30.719909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:37.638905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:44.673271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:52.821148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:42.792859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:50.106386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:04:57.111355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:04.040619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:11.276631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:17.579807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:24.575194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:31.346500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:38.288672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-09T17:05:45.302044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-05-09T17:06:08.500892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
mother_body_mass_indexmother_delivery_weightmother_racemother_heightmother_weight_gainfather_agefather_educationcigarettes_before_pregnancyprenatal_care_monthnumber_prenatal_visitsnewborn_weightmother_marital_statusprevious_cesareannewborn_gender
mother_body_mass_index1.0000.8090.001-0.043-0.2600.009-0.1210.0100.0010.0340.0910.0880.0840.000
mother_delivery_weight0.8091.000-0.0380.3600.1720.026-0.0480.031-0.0220.0890.2120.0760.0590.011
mother_race0.001-0.0381.000-0.050-0.0510.0440.105-0.0490.049-0.081-0.1350.3080.0180.005
mother_height-0.0430.360-0.0501.0000.1260.0830.1170.024-0.0250.0520.1600.0820.0340.000
mother_weight_gain-0.2600.172-0.0510.1261.000-0.0240.0550.022-0.0340.0880.1730.0950.0300.027
father_age0.0090.0260.0440.083-0.0241.0000.279-0.066-0.0390.0470.0330.3260.0820.003
father_education-0.121-0.0480.1050.1170.0550.2791.000-0.026-0.0230.010-0.0100.5530.0220.003
cigarettes_before_pregnancy0.0100.031-0.0490.0240.022-0.066-0.0261.0000.049-0.057-0.0790.1630.0110.000
prenatal_care_month0.001-0.0220.049-0.025-0.034-0.039-0.0230.0491.000-0.324-0.0100.0210.0200.000
number_prenatal_visits0.0340.089-0.0810.0520.0880.0470.010-0.057-0.3241.0000.1490.1360.0190.008
newborn_weight0.0910.212-0.1350.1600.1730.033-0.010-0.079-0.0100.1491.0000.1130.0240.109
mother_marital_status0.0880.0760.3080.0820.0950.3260.5530.1630.0210.1360.1131.0000.0320.002
previous_cesarean0.0840.0590.0180.0340.0300.0820.0220.0110.0200.0190.0240.0321.0000.000
newborn_gender0.0000.0110.0050.0000.0270.0030.0030.0000.0000.0080.1090.0020.0001.000

Missing values

2023-05-09T17:05:53.573662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-09T17:05:56.443981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-09T17:06:03.589809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

mother_body_mass_indexmother_marital_statusmother_delivery_weightmother_racemother_heightmother_weight_gainfather_agefather_educationcigarettes_before_pregnancyprenatal_care_monthnumber_prenatal_visitsprevious_cesareannewborn_gendernewborn_weight
030.82.0220.0165.035.029.060.0210.0NF3045
145.8NaN293.0164.026.037.040.0310.0NF3061
2NaN1.0NaN166.0NaN33.060.03NaNNF3827
324.31.0157.01NaN20.027.060.039.0NM3997
424.11.0187.0165.042.029.080.0212.0NF3240
530.92.0231.01NaN51.027.030.0410.0NM3544
622.91.0141.01NaN16.033.030.048.0NM3010
728.3NaN182.0165.012.0NaN511.0315.0NM3856
840.71.0NaN163.0NaN37.040.039.0YM1015
936.31.0274.0171.014.033.030.0214.0NF4450
mother_body_mass_indexmother_marital_statusmother_delivery_weightmother_racemother_heightmother_weight_gainfather_agefather_educationcigarettes_before_pregnancyprenatal_care_monthnumber_prenatal_visitsprevious_cesareannewborn_gendernewborn_weight
239810631.2NaN172.0160.012.025.010.0215.0NF2745
239810722.92.0157.0262.032.035.060.0212.0NF3080
239810838.61.0253.0464.028.035.070.0312.0YM3997
239810923.31.0170.0465.030.034.080.027.0NF3165
2398110NaN2.0217.0165.071.036.040.0213.0NM3986
239811122.11.0152.0163.027.0NaN40.045.0NM3015
239811234.02.0260.0271.016.033.030.0113.0NM3572
239811324.61.0157.01NaN18.026.040.0315.0NF3299
239811426.1NaN185.0161.047.031.010.0215.0NM3062
239811523.01.0172.0463.042.043.060.059.0YM3660

Duplicate rows

Most frequently occurring

mother_body_mass_indexmother_marital_statusmother_delivery_weightmother_racemother_heightmother_weight_gainfather_agefather_educationcigarettes_before_pregnancyprenatal_care_monthnumber_prenatal_visitsprevious_cesareannewborn_gendernewborn_weight# duplicates
353NaNNaNNaN4NaNNaNNaN9NaN99NaNNF35003
015.22.0164.0168.064.0NaN90.0312.0NM15032
117.41.0137.0162.042.030.060.0240.0YM21502
217.62.0122.0160.032.018.020.057.0NF24382
317.91.0162.0168.044.037.060.0215.0NF29202
418.01.0152.0167.037.026.040.039.0NF24662
518.02.0129.0160.037.029.0315.0314.0NM20102
618.21.0170.0168.050.034.080.0312.0NM26702
718.32.0135.0662.035.0NaN90.022.0NM14182
818.32.0138.0265.028.0NaN90.0312.0NM33852